import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


lowerCamelCase__  =					logging.get_logger(__name__)

lowerCamelCase__  =					{'''vocab_file''': '''spiece.model'''}

lowerCamelCase__  =					{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   Any							, lowercase_				:   List[Any]							, lowercase_				:   List[str]=False							, lowercase_				:   List[str]=True							, lowercase_				:   Tuple=False							, lowercase_				:   List[str]="<s>"							, lowercase_				:   Dict="</s>"							, lowercase_				:   Any="<unk>"							, lowercase_				:   List[Any]="<sep>"							, lowercase_				:   List[str]="<pad>"							, lowercase_				:   Dict="<cls>"							, lowercase_				:   List[Any]="<mask>"							, lowercase_				:   Union[str, Any]=["<eop>", "<eod>"]							, lowercase_				:   Optional[Dict[str, Any]] = None							, **lowercase_				:   str							, )						->	None:


   """simple docstring"""







   _UpperCamelCase           =    AddedToken(lowercase_							, lstrip=lowercase_							, rstrip=lowercase_) if isinstance(lowercase_							, lowercase_) else mask_token

   _UpperCamelCase           =    {} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       do_lower_case=lowercase_							, remove_space=lowercase_							, keep_accents=lowercase_							, bos_token=lowercase_							, eos_token=lowercase_							, unk_token=lowercase_							, sep_token=lowercase_							, pad_token=lowercase_							, cls_token=lowercase_							, mask_token=lowercase_							, additional_special_tokens=lowercase_							, sp_model_kwargs=self.sp_model_kwargs							, **lowercase_							, )

   _UpperCamelCase           =    3

   _UpperCamelCase           =    do_lower_case
   _UpperCamelCase           =    remove_space
   _UpperCamelCase           =    keep_accents
   _UpperCamelCase           =    vocab_file

   _UpperCamelCase           =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(lowercase_)

   try:
     import jieba
   except ModuleNotFoundError as error:
     raise error.__class__(
         "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
         "See https://pypi.org/project/jieba/ for installation.")
   _UpperCamelCase           =    jieba
   _UpperCamelCase           =    str.maketrans(" \n"							, "\u2582\u2583")







 @property
 # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	str:


   """simple docstring"""







   return len(self.sp_model)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    {self.convert_ids_to_tokens(lowercase_): i for i in range(self.vocab_size)}
   vocab.update(self.added_tokens_encoder)
   return vocab







 def __getstate__(		self				:   Union[str, Any])						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.__dict__.copy()
   _UpperCamelCase           =    None
   return state







 def __setstate__(		self				:   Optional[Any]							, lowercase_				:   Union[str, Any])						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    d

   # for backward compatibility
   if not hasattr(self							, "sp_model_kwargs"):
     _UpperCamelCase           =    {}

   _UpperCamelCase           =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(self.vocab_file)







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   Any)						->	Tuple:


   """simple docstring"""







   if self.remove_space:
     _UpperCamelCase           =    " ".join(inputs.strip().split())
   else:
     _UpperCamelCase           =    inputs
   _UpperCamelCase           =    outputs.replace("``"							, "\"").replace("''"							, "\"")

   if not self.keep_accents:
     _UpperCamelCase           =    unicodedata.normalize("NFKD"							, lowercase_)
     _UpperCamelCase           =    "".join([c for c in outputs if not unicodedata.combining(lowercase_)])
   if self.do_lower_case:
     _UpperCamelCase           =    outputs.lower()

   return outputs







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   str)						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    self.preprocess_text(lowercase_)
   _UpperCamelCase           =    self.sp_model.encode(lowercase_							, out_type=lowercase_)
   _UpperCamelCase           =    []
   for piece in pieces:
     if len(lowercase_) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
       _UpperCamelCase           =    self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase_							, ""))
       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
         if len(cur_pieces[0]) == 1:
           _UpperCamelCase           =    cur_pieces[1:]
         else:
           _UpperCamelCase           =    cur_pieces[0][1:]
       cur_pieces.append(piece[-1])
       new_pieces.extend(lowercase_)
     else:
       new_pieces.append(lowercase_)

   return new_pieces







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Dict)						->	Dict:


   """simple docstring"""







   return self.sp_model.PieceToId(lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   Optional[int])						->	Any:


   """simple docstring"""







   return self.sp_model.IdToPiece(lowercase_)







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   Any)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    "".join(lowercase_).replace(lowercase_							, " ").strip()
   return out_string







 def 		__UpperCAmelCase				(		self				:   Dict							, lowercase_				:   List[int]							, lowercase_				:   Optional[List[int]] = None)						->	List[int]:


   """simple docstring"""







   _UpperCamelCase           =    [self.sep_token_id]
   _UpperCamelCase           =    [self.cls_token_id]
   if token_ids_a is None:
     return token_ids_a + sep + cls
   return token_ids_a + sep + token_ids_a + sep + cls







 def 		__UpperCAmelCase				(		self				:   Dict							, lowercase_				:   List[int]							, lowercase_				:   Optional[List[int]] = None							, lowercase_				:   bool = False)						->	List[int]:


   """simple docstring"""







   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=lowercase_							, token_ids_a=lowercase_							, already_has_special_tokens=lowercase_)

   if token_ids_a is not None:
     return ([0] * len(lowercase_)) + [1] + ([0] * len(lowercase_)) + [1, 1]
   return ([0] * len(lowercase_)) + [1, 1]







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   List[int]							, lowercase_				:   Optional[List[int]] = None)						->	List[int]:


   """simple docstring"""







   _UpperCamelCase           =    [self.sep_token_id]
   _UpperCamelCase           =    [2]

   if token_ids_a is None:
     return len(token_ids_a + sep) * [0] + cls_segment_id
   return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   str							, lowercase_				:   Optional[str] = None)						->	Tuple[str]:


   """simple docstring"""







   if not os.path.isdir(lowercase_):
     logger.error(f'Vocabulary path ({save_directory}) should be a directory')
     return
   _UpperCamelCase           =    os.path.join(
       lowercase_							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

   if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_) and os.path.isfile(self.vocab_file):
     copyfile(self.vocab_file							, lowercase_)
   elif not os.path.isfile(self.vocab_file):
     with open(lowercase_							, "wb") as fi:
       _UpperCamelCase           =    self.sp_model.serialized_model_proto()
       fi.write(lowercase_)

   return (out_vocab_file,)







 def 		__UpperCAmelCase				(		self				:   int							, *lowercase_				:   List[str]							, **lowercase_				:   Optional[Any])						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    super()._decode(*lowercase_							, **lowercase_)
   _UpperCamelCase           =    text.replace(" "							, "").replace("\u2582"							, " ").replace("\u2583"							, "\n")
   return text





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''DebertaTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DebertaForMaskedLM''',
       '''DebertaForQuestionAnswering''',
       '''DebertaForSequenceClassification''',
       '''DebertaForTokenClassification''',
       '''DebertaModel''',
       '''DebertaPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFDebertaForMaskedLM''',
       '''TFDebertaForQuestionAnswering''',
       '''TFDebertaForSequenceClassification''',
       '''TFDebertaForTokenClassification''',
       '''TFDebertaModel''',
       '''TFDebertaPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




